Disability effects on daily activity type and duration

Document Type : Article

Authors

Department of Civil Engineering, Sharif University of Technology, Tehran, Iran

Abstract

Equity concerns of urban planners and policy-makers could not be addressed unless disability effects on daily activities are disentangled. The findings, however, strongly depend on how disability is incorporated into the model. Two MDCEV models for analyzing disability effects on daily activity type and duration are discussed and compared in this paper. In the “classic” approach, an independent dummy variable is used to distinguish disability. While, in the “separate” approach, the dataset is divided into disabled and non-disabled groups, and then a separate model is calibrated for the disabled group. The two approaches result in different coefficients and elasticity values, evidencing that model specification matters for policy assessments. Three transferability metrics are adopted to evidence that the separate approach outperforms the classic approach in explaining travel pattern of persons with disabilities. Finally, three policies that have been practiced across the globe to prevent social exclusion of disabled people are discussed in terms of the effects of model specification on the policy assessment outcomes. This assessment offers managerial insights for policy-makers to develop appropriate infrastructure and accessibility strategies to the disabled people.

Keywords

References

  1. World Bank, Disability inclusion, Last Updated: Sep 26, 2018. https://www.worldbank.org/en/topic/disability#1. Accessed Mar 20, 2019.
  2. Var, T., Yeşiltaş, M., Yaylı, A., et al. “A study on the travel patterns of physically disabled people”, Asia Pacific Journal of Tourism Research, 16(6), 599-618 (2011).
  3. Ermagun, A., Hajivosough, S., Samimi, A., et al. “A joint model for trip purpose and escorting patterns of the disabled”, Travel Behaviour and Society, 3, 51-58 (2016).
  4. Bureau of Transportation Statistics, U.S. Department of Transportation. “Transportation Difficulties Keep Over Half a Million Disabled at Home”, (2003). http://www.bts.gov/publications/special_reports_and_issues_ briefs/issue_briefs/number_03/pdf/entire.pdf
  5. Jolly, D., Priestley, M., & Matthews, B. “Secondary analysis of existing data on disabled people’s use and experiences of public transport in Great Britain”, Disability Rights Commission (2006).
  6. Carreno, M., Rye, T., Stradling, S., et al. “Barriers to Public Transport Use: From the Perspective of Disabled Scottish Adults”, In 11th World Conference on Transport Research (2007).‏
  7. Mayor of London. “Understanding the travel needs of London’s diverse communities, Disabled People”, Transport for London, Tfl no. 10038 (2012).
  8. Sweeney, M. “Travel patterns of older Americans with disabilities”, Washington, DC: US Bureau of Transportation Statistics (2004).
  9. Mayor of London. “Travel in London: understanding our diverse communities”, Transport for London (2015).
  10. Taylor, Z., & Józefowicz, I. “Intra-urban daily mobility of disabled people for recreational and leisure purposes”, Journal of Transport Geography, 24, 155-172 (2012).
  11. Freedman, V. A., and J. C. Cornman. “The Panel Study of Income of Dynamics’ Supplement on Disability and Use of Time (DUST) User Guide”, Institute for Social Research, University of Michigan, Ann Arbor (2012).
  12. Castro, M., Bhat, C. R., Pendyala, R. M., et al. “Accommodating multiple constraints in the multiple discrete–continuous extreme value (MDCEV) choice model”, Transportation Research Part B: Methodological, 46(6), 729-743 (2012).
  13. Schepers, J. “On regression modelling with dummy variables versus separate regressions per group: Comment on Holgersson et al.”, Journal of Applied Statistics, 43(4), 674-681 (2016).
  14. Holgersson, H. E. T., Nordström, L., & Öner, Ö. “Dummy variables vs. category-wise models”, Journal of Applied Statistics, 41(2), 233-241 (2014).
  15. Amendola, A., Dell’Anno, R., & Parisi, L. “Happiness and inequality in European countries: is it a matter of peer group comparisons?”, Economia Politica, 36(2), 473-508 (2019).
  16. Driver, C., & Muñoz-Bugarin, J. “Financial constraints on investment: Effects of firm size and the financial crisis”, Research in International Business and Finance, 47, 441-457(2019)..
  17. Loesch, E. K. “Using Remote Sensing and Spatial Statistics to Characterize Increased Seismicity in Oklahoma”, Doctoral dissertation, Saint Louis University (2018).
  18. Gezahegn, T. W., Van Passel, S., Berhanu, T., et al. “Do bottom-up and independent agricultural cooperatives really perform better? Insights from a technical efficiency analysis in Ethiopia”, Agrekon, 59(1), 93-109 (2020).
  19. Shabanpour, R., Golshani, N., Fasihozaman Langerudi, M., et al. “Planning in-home activities in the ADAPTS activity-based model: a joint model of activity type and duration”, International Journal of Urban Sciences, 1-19 (2017).
  20. Páez, A., & Farber, S. “Participation and desire: leisure activities among Canadian adults with disabilities”, Transportation, 39(6), 1055-1078 (2012).
  21. World health organization, Model Disability Survey. http://www.who.int/disabilities/data/mds/en/. (Accessed May, 2018).
  22. Statistics Canada, The 2006 Participation and Activity Limitation Survey: Disability in Canada. http://www5.statcan.gc.ca/olc-cel/olc.action?objId=89-628-X&objType=2&lang=en&limit=1. (Accessed April, 2018).
  23. United Nation Conference of States Parties to the Convention on the Rights of Persons with Disabilities.  “Improvement of disability data and statistics: objectives and challenges”, Eighth session, New York, (9 – 11 June 2015). www.un.org/disabilities/documents/COP/crpd_csp_2015_3.doc.
  24. Bhat, C. R., Srinivasan, S., & Sen, S. “A joint model for the perfect and imperfect substitute goods case: application to activity time-use decisions”, Transportation Research Part B: Methodological, 40(10), 827-850 (2006).
  25. Pinjari, A. R., Bhat, C. R., & Hensher, D. A. “Residential self-selection effects in an activity time-use behavior model”, Transportation Research Part B: Methodological, 43(7), 729-748 (2009).
  26. Stern, S. “A disaggregate discrete choice model of transportation demand by elderly and disabled people in rural Virginia”, Transportation Research Part A: Policy and Practice, 27(4), 315-327 (1993).
  27. Schmöcker, J. D., Quddus, M., Noland, R., et al. “Estimating trip generation of elderly and disabled people: analysis of London data”, Transportation Research Record: Journal of the Transportation Research Board, (1924), 9-18 (2005).
  28. Pinjari, A. R., & Bhat, C. A. “Multiple Discrete–Continuous Nested Extreme Value (MDCNEV) model: formulation and application to non-worker activity time-use and timing behavior on weekdays”, Transportation Research Part B: Methodological, 44(4), 562-583 (2010).
  29. Sharifi, M. S., Stuart, D., Christensen, K., et al. “Analysis of walking speeds involving individuals with disabilities in different indoor walking environments”, Journal of Urban Planning and Development, 142(1), 04015010 (2015).
  30. Ravulaparthy, S., Yoon, S., & Goulias, K. “Linking elderly transport mobility and subjective well-Being: A multivariate latent modeling approach”, Transportation Research Record: Journal of the Transportation Research Board, (2382), 28-36 (2013).
  31. Atlanta Regional Commission. “Regional Travel Survey Final Report”, Atlanta Regional Commission, GA (2011).
  32. Nustats, P. T. V. “Atlanta regional commission regional travel survey final report”, Atlanta Regional Commission, 1-207 (2011).
  33. Ermagun, A., & Samimi, A. “Mode choice and travel distance joint models in school trips”, Transportation, 1-27 (2017).
  34. Liu, C., Susilo, Y. O., & Karlström, A. “Jointly modelling individual’s daily activity-travel time use and mode share by a nested multivariate Tobit model system”, Transportmetrica A: Transport Science, 13(6), 491-518 (2017).
  35. Ding, C., Mishra, S., Lin, Y., et al. “Cross-nested joint model of travel mode and departure time choice for urban commuting trips: Case study in Maryland–Washington, DC Region”, Journal of Urban Planning and Development, 141(4), 04014036 (2014).
  36. Habib, K. M. N. “A joint discrete-continuous model considering budget constraint for the continuous part: application in joint mode and departure time choice modelling”, Transportmetrica A: Transport Science, 9(2), 149-177 (2013).
  37. Jovicic, G. “Activity based travel demand modelling”, Danmarks Transp. Skn (2001).
  38. Eluru, N., Pinjari, A. R., Pendyala, R. M., et al. “Unified Model System of Activity Type Choice, Activity Duration, Activity Timing, Mode Choice, and Destination Choice”, Working Paper, the University of Texas at Austin, Texas (2009).
  39. Bhat, C. R. “A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions”, Transportation Research Part B: Methodological, 39(8), 679-707 (2005).
  40. Jian, S., Rashidi, T. H., & Dixit, V. “An analysis of carsharing vehicle choice and utilization patterns using multiple discrete-continuous extreme value (MDCEV) models”, Transportation Research Part A: Policy and Practice, 103, 362-376 (2017).
  41. Bhat, C. R., & Sen, S. “Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model”, Transportation Research Part B: Methodological, 40(1), 35-53 (2006).
  42. Ma, J., & Ye, X. “Modeling Household Vehicle Ownership in Emerging Economies”, Journal of the Indian Institute of Science, 1-25 (2019).
  43. Ma, J., Ye, X., & Pinjari, A. R. “Practical Method to Simulate Multiple Discrete-Continuous Generalized Extreme Value Model: Application to Examine Substitution Patterns of Household Transportation Expenditures”, Transportation Research Record, 2673(8), 145-156 (2019).
  44. Zong, W., Zhang, J., Yu, B., et al. “Energy consumption in the transport and domestic sectors: a household-level comparison between capital cities of Japan, China, and Indonesia”, In Transport and Energy Research (pp. 73-98). Elsevier (2020).
  45. Bhat, C. R. “The multiple discrete-continuous extreme value (MDCEV) model: role of utility function parameters, identification considerations, and model extensions”, Transportation Research Part B: Methodological, 42(3), 274-303 (2008).
  46. Bhat, C. R. “MDCEV with Outside Good”, public code, Available on http://www.caee.utexas.edu/prof/bhat/CodeRepository/CODES/MDCEV_Files/MDCEV_With_Outside_Good.zip (2008).
  47. Shamshiripour, A., & Samimi, A. “Estimating a mixed-profile MDCEV: case of daily activity type and duration”, Transportation Letters, 11(6), 289-302 (2019).
  48. Pinjari, A. R., & Bhat, C. R. “An efficient forecasting procedure for Kuhn-Tucker consumer demand model systems: application to residential energy consumption analysis”, Technical paper, Department of Civil and Environmental Engineering, University of South Florida, 263-285 (2010).
  49. Atherton, T. J., & Ben-Akiva, M. “Transferability and updating of disaggregate travel demand models”, (No. 610) (1976).
  50. Koppelman, F. S., & Wilmot, C. G. “Transferability analysis of disaggregate choice models”, Transportation Research Record, 895, 18-24 (1982).
  51. McFadden, D. “Conditional logit analysis of qualitative choice behavior”, (1973).
  52. Milakis, D., & van Wee, B. “Implications of vehicle automation for accessibility and social inclusion of people on low income, people with physical and sensory disabilities, and older people”, In Demand for Emerging Transportation Systems (pp. 61-73). Elsevier (2020).
  53. Bennett, R., Vijaygopal, R., & Kottasz, R. “Willingness of people who are blind to accept autonomous vehicles: An empirical investigation”, Transportation Research Part F: Traffic Psychology and Behaviour, 69, 13-27 (2020)..
  54. Queensland Health, Medical Aids Subsidy Scheme (MASS), “Vehicle Options Subsidy Scheme (VOSS) Guidelines”, (Accessed April, 2018). https://www.ideas.org.au/uploads/resources/769/VOSS%20Guidelines%20QLD.pdf
  55. Ren, G., Zhang, T., Xu, L., et al. “Transportation Demands of Low-Mobility Individuals: Case Study in Wenling, China”, Journal of Urban Planning and Development, 144(4), 05018019 (2018).
  56. Jansuwan, S., Christensen, K. M., & Chen, A. “Assessing the transportation needs of low-mobility individuals: Case study of a small urban community in Utah”, Journal of Urban Planning and Development, 139(2), 104-114 (2013).
  57. Bezyak, J. L., Sabella, S., Hammel, J., et al. “Community participation and public transportation barriers experienced by people with disabilities”, Disability and rehabilitation, 1-9 (2019).
  58. CRT (Country Roads Transit) agency, https://countryroadstransit.com/about/. (Accessed April, 2018).
  59. Pfeiffer, B., Sell, A., & Bevans, K. B. “Initial evaluation of a public transportation training program for individuals with intellectual and developmental disabilities”, Journal of Transport & Health, 16, 100813 (2020).
  60. Government of Canada. “Canada Student Grant for Students with Permanent Disabilities”, https://www.canada.ca/en/employment-social-development/services/education/grants/disabilities.html (Accessed April, 2018).
  61. University of Iowa. “Transit Services for Persons with Disabilities”, https://transportation.uiowa.edu/transit-services-persons-disabilities-bionic-bus (Accessed April, 2018).
Scientia Iranica
Volume 28, Issue 4
Transactions on Civil Engineering (A)
July and August 2021
Pages 1994-2009

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